A Fast Parallelized Computational Approach Based on Sparse LU Factorization for Predictions of Spatial and Time-Dependent Currents and Voltages in Full-Body Biomodels

Abstract

Realistic and accurate numerical simulations of electrostimulation of tissues and full-body biomodels have been developed and implemented. Typically, whole-body systems are very complex and consist of a multitude of tissues, organs, and subcomponents with diverse properties. From an electrical standpoint, these can be characterized in terms of separate conductivities and permittivities. Accuracy demands good spatial resolution; thus, the overall tissue/animal models need to be discretized into a fine-grained mesh. This can lead to a large number of grid points (especially for a three-dimensional entity) and can place prohibitive requirements of memory storage and execution times on computing machines. Here, the authors include a simple yet fast and efficient numerical implementation. It is based on LU decomposition for execution on a cluster of computers running in parallel with distributed storage of the data in a sparse format. In this paper, the details of electrical tissue representation, the fast algorithm, the relevant biomodels, and specific applications to whole-animal studies of electrostimulation are discussed